Python | Read Text from Image with One Line Code .While image classification and tasks that involve some level of computer vision might require a good bit of code and a solid understanding, reading text from a somewhat well-formatted image turns out to be a one-liner in Python
Dealing with images is not a trivial task. To you, as a human, it’s easy to look at something and immediately know what is it you’re looking at. But computers don’t work that way.
Tasks that are too hard for you, like complex arithmetics, and math in general, is something that a computer chews without breaking a sweat. But here the exact opposite applies — tasks that are trivial to you, like recognizing is it cat or dog in an image are really hard for a computer. In a way, we are a perfect match. For now at least.
While image classification and tasks that involve some level of computer vision might require a good bit of code and a solid understanding, reading text from a somewhat well-formatted image turns out to be a one-liner in Python —and can be applied to so many real-life problems.
And in today’s post, I want to prove that claim. There will be some installation to go though, but it shouldn’t take much time. These are the libraries you’ll need:
I don’t want to prolonge this intro part anymore, so why don’t we jump into the good stuff now.
Now, this library will only be used to load the images(s), you don’t actually need to have a solid understanding of it beforehand (although it might be helpful, you’ll see why).
According to the official documentation:
OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products. Being a BSD-licensed product, OpenCV makes it easy for businesses to utilize and modify the code.
In a nutshell, you can use OpenCV to do any kind of image transformations, it’s fairly straightforward library.
If you don’t already have it installed, it’ll be just a single line in terminal:
pip install opencv-python
And that’s pretty much it. It was easy up until this point, but that’s about to change.
What the heck is this library? Well, according to Wikipedia:
Tesseract is an optical character recognition engine for various operating systems. It is free software, released under the Apache License, Version 2.0, and development has been sponsored by Google since 2006.
I’m sure there are more sophisticated libraries available now, but I’ve found this one working out pretty well. Based on my own experience, this library should be able to read text from any image, provided that the font isn’t some bulls*** that even you aren’t able to read.
If it can’t read from your image, spend more time playing around with OpenCV, applying various filters to make the text stand out.
Now the installation is a bit of a pain in the bottom. If you are on Linux it all boils down to a couple of sudo-apt get commands:
sudo apt-get update sudo apt-get install tesseract-ocr sudo apt-get install libtesseract-dev
I’m on Windows, so the process is a bit more tedious.
First, open up THIS URL, and download 32bit or 64bit installer:
The installation by itself is straightforward, boils down to clicking Next a couple of times. And yeah, you also need to do a pip installation:
pip install pytesseract
Is that all? Well, no. You still need to tell Python where Tesseract is installed. On Linux machines, I didn’t have to do so, but it’s required on Windows. By default, it’s installed in Program Files.
If you did everything correctly, executing this cell should not yield any error:
Is everything good? You may proceed.
Let’s start with a simple one. I’ve found a couple of royalty-free images that contain some sort of text, and the first one is this:
It should be the easy one, and there exists a possibility that Tesseract will read those blue ‘objects’ as brackets. Let’ see what will happen:
My claim was true. It’s not a problem though, you could easily address those with some Python magic.
The next one could be more tricky:
I hope it won’t detect that ‘B’ on the coin:
Looks like it works perfectly.
Now it’s up to you to apply this to your own problem. OpenCV skills could be of vital importance here if the text blends with the background.
Reading text from an image is a pretty difficult task for a computer to perform. Think about it, the computer doesn’t know what a letter is, it only works only with numbers. What happens behind the hood might seem like a black box at first, but I encourage you to investigate further if this is your area of interest.
I’m not saying that PyTesseract will work perfectly every time, but I’ve found it good enough even on some trickier images. But not straight out of the box. Some image manipulation is required to make the text stand out.
It’s a complex topic, I know. Take it one day at a time. One day it will be second nature to you.
In this post, we will introduce a functional programming model. We learn about lambda expressions in Python, important function functions and the concept of particles.
Most of us have been introduced to Python as an
object-oriented language, but Python functions are also useful tools for data scientists and programmers alike. While classes, and objects, are easy to start working with, there are other ways to write your Python code. Languages like Java can make it hard to move away from object-oriented thinking, but Python makes it easy.
Given that Python facilitates different approaches to writing code, a logical follow-up question is: what is a different way to write code? While there are several answers to this question, the most common alternative style of writing code is called
functional programming. Functional programming gets its name from writing functions which provides the main source of logic in a program.
In this post, we will:
Explain the basics of functional programming by comparing it to object-oriented programming.
Cover why you might want to incorporate functional programming in your own code.
Show you how Python allows you to switch between the two.
The easiest way to introduce functional programming is to compare it to something we’re already aware of: object-oriented programming. Suppose we wanted to create a line counter class that takes in a file, reads each line, then counts the total amount of lines in the file. Using a class, it could look something like the following:
class LineCounter: def __init__(self, filename): self.file = open(filename, 'r') self.lines =  def read(self): self.lines = [line for line in self.file] def count(self): return len(self.lines)
While not the best implementation, it does provide an insight into object-oriented design. Within the class, there are the familiar concepts of methods and properties. The properties set and retrieve the state of the object, and the methods manipulate that state.
For both these concepts to work, the object’s state must change over time. This change of state is evident in the
lines property after calling the
read() method. As an example, here’s how we would use this class:
# example_file.txt contains 100 lines. lc = LineCounter('example_file.txt') print(lc.lines) >>  print(lc.count()) >> 0 # The lc object must read the file to # set the lines property. lc.read() # The `lc.lines` property has been changed. # This is called changing the state of the lc # object. print(lc.lines) >> [['Hello world!', ...]] print(lc.count()) >> 100
The ever-changing state of an object is both its blessing and curse. To understand why a changing state can be seen as a negative, we have to introduce an alternative. The alternative is to build the line counter as a series of independent functions.
Working with pure functions
def read(filename): with open(filename, 'r') as f: return [line for line in f] def count(lines): return len(lines) example_lines = read('example_log.txt') lines_count = count(example_lines)
In the previous example, we were able to count the lines only with the use of functions. When we only use functions, we are applying a functional approach to programming which is, non-excitingly, called functional programming. The concepts behind functional programming requires functions to be
stateless, and rely only on their given inputs to produce an output.
The functions that meet the above criteria are called
pure functions. Here’s an example to highlight the difference between pure functions, and non-pure:
# Create a global variable `A`. A = 5 def impure_sum(b): # Adds two numbers, but uses the # global `A` variable. return b + A def pure_sum(a, b): # Adds two numbers, using # ONLY the local function inputs. return a + b print(impure_sum(6)) >> 11 print(pure_sum(4, 6)) >> 10
The benefit of using pure functions over impure (non-pure) functions is the reduction of side effects. **Side effects **occur when there are changes performed within a function’s operation that are outside its scope. For example, they occur when we change the state of an object, perform any I/O operation, or even call
def read_and_print(filename): with open(filename) as f: # Side effect of opening a # file outside of function. data = [line for line in f] for line in data: # Call out to the operating system # "println" method (side effect). print(line)
Programmers reduce side effects in their code to make it easier to follow, test, and debug. The more side effects a codebase has, the harder it is to step through a program and understand its sequence of execution.
While it’s convienent to try and eliminate all side effects, they’re often used to make programming easier. If we were to ban all side effects, then you wouldn’t be able to read in a file, call print, or even assign a variable within a function. Advocates for functional programming understand this tradeoff, and try to eliminate side effects where possible without sacrificing development implementation time.The Lambda Expression
Instead of the
def syntax for function declaration, we can use a lambda expression to write Python functions. The lambda syntax closely follows the
def syntax, but it’s not a 1-to-1 mapping. Here’s an example of building a function that adds two integers:
# Using `def` (old way). def old_add(a, b): return a + b # Using `lambda` (new way). new_add = lambda a, b: a + bold_add(10, 5) == new_add(10, 5) >> True
lambda expression takes in a comma seperated sequences of inputs (like
def). Then, immediately following the colon, it returns the expression without using an explicit return statement. Finally, when assigning the
lambda expression to a variable, it acts exactly like a Python function, and can be called using the the function call syntax:
If we didn’t assign
lambda to a variable name, it would be called an anonymous function. These** anonymous functions** are extremely helpful, especially when using them as an input for another function. For example, the
sorted() function takes in an optional
key argument (a function) that describes how the items in a list should be sorted.
The Map Function
unsorted = [('b', 6), ('a', 10), ('d', 0), ('c', 4)] # Sort on the second tuple value (the integer). print(sorted(unsorted, key=lambda x: x)) >> [('d', 0), ('c', 4), ('b', 6), ('a', 10)]
While the ability to pass in functions as arguments is not unique to Python, it is a recent development in programming languages. Functions that allow for this type of behavior are called** first-class functions**. Any language that contains first-class functions can be written in a functional style.
There are a set of important first-class functions that are commonly used within the functional paradigm. These functions take in a Python iterable, and, like
sorted(), apply a function for each element in the list. Over the next few sections, we will examine each of these functions, but they all follow the general form of
The first function we’ll work with is the
map() function. The
map() function takes in an iterable (ie.
list), and creates a new iterable object, a special
map object. The new object has the first-class function applied to every element.
# Pseudocode for map. def map(func, seq): # Return `Map` object with # the function applied to every # element. return Map( func(x) for x in seq )
Here’s how we could use map to add
20 to every element in a list:
values = [1, 2, 3, 4, 5] # Note: We convert the returned map object to # a list data structure. add_10 = list(map(lambda x: x + 10, values)) add_20 = list(map(lambda x: x + 20, values)) print(add_10) >> [11, 12, 13, 14, 15] print(add_20) >> [21, 22, 23, 24, 25]
Note that it’s important to cast the return value from
map() as a
list object. Using the returned
map object is difficult to work with if you’re expecting it to function like a
list. First, printing it does not show each of its items, and secondly, you can only iterate over it once.
The second function we’ll work with is the
filter() function. The
filter() function takes in an iterable, creates a new iterable object (again, a special
map object), and a first-class function that must return a
bool value. The new map object is a filtered iterable of all elements that returned
# Pseudocode for filter. def filter(evaluate, seq): # Return `Map` object with # the evaluate function applied to every # element. return Map( x for x in seq if evaluate(x) is True )
Here’s how we could filter odd or even values from a list:
The Reduce Function
values = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] # Note: We convert the returned filter object to # a list data structure. even = list(filter(lambda x: x % 2 == 0, values)) odd = list(filter(lambda x: x % 2 == 1, values)) print(even) >> [2, 4, 6, 8, 10] print(odd) >> [1, 3, 5, 7, 9]
The last function we’ll look at is the reduce() function from the functools package. The
reduce() function takes in an iterable, and then reduces the iterable to a single value. Reduce is different from
map(), because reduce() takes in a function that has two input values.
Here’s an example of how we can use
reduce() to sum all elements in a list.
from functools import reduce values = [1, 2, 3, 4] summed = reduce(lambda a, b: a + b, values) print(summed) >> 10
An interesting note to make is that you do not have to operate on the second value in the
lambda expression. For example, you can write a function that always returns the first value of an iterable:
Rewriting with list comprehensions
from functools import reduce values = [1, 2, 3, 4, 5] # By convention, we add `_` as a placeholder for an input # we do not use. first_value = reduce(lambda a, _: a, values) print(first_value) >> 1
Because we eventually convert to lists, we should rewrite the
filter() functions using list comprehension instead. This is the more pythonic way of writing them, as we are taking advantage of the Python syntax for making lists. Here’s how you could translate the previous examples of
filter() to list comprehensions:
values = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] # Map. add_10 = [x + 10 for x in values] print(add_10) >> [11, 12, 13, 14, 15, 16, 17, 18, 19, 20] # Filter. even = [x for x in values if x % 2 == 0] print(even) >> [2, 4, 6, 8, 10]
From the examples, you can see that we don’t need to add the lambda expressions. If you are looking to add
filter() functions to your own code, this is usually the recommended way. However, in the next section, we’ll provide a case to still use the
Sometimes we want to use the behavior of a function, but decrease the number of arguments it takes. The purpose is to “save” one of the inputs, and create a new function that defaults the behavior using the saved input. Suppose we wanted to write a function that would always add 2 to any number:
def add_two(b): return 2 + b print(add_two(4)) >> 6
add_two function is similar to the general function, $f(a,b) = a + b$, only it defaults one of the arguments ($a = 2$). In Python, we can use the partial module from the
functools package to set these argument defaults. The
partial module takes in a function, and “freezes” any number of args (or kwargs), starting from the first argument, then returns a new function with the default inputs.
from functools import partialdef add(a, b): return a + b add_two = partial(add, 2) add_ten = partial(add, 10) print(add_two(4)) >> 6 print(add_ten(4)) >> 14
Partials can take in any function, including ones from the standard library.
# A partial that grabs IP addresses using # the `map` function from the standard library. extract_ips = partial( map, lambda x: x.split(' ') ) lines = read('example_log.txt') ip_addresses = list(extract_ip(lines))
In this post, we introduced the paradigm of functional programming. We learned about the lambda expression in Python, important functional functions, and the concept of partials. Overall, we showed that Python provides a programmer with the tools to easily switch between functional programming and object-oriented programming.
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